The efficacy of these actions are limited by our ability to monitor and predict the evolutionary dynamics in response to interventions. Evolution is shaped by a multitude of stochastic effects, including the stochasticity in the rise of novel beneficial mutations and fluctuations in the environment, which at best, raise skepticism about predicting evolution [1]}, [2]}. However, evolutionary predictability is not an absolute concept and it depends strongly on the time window and the molecular features that we are interested in. For example, despite a rapid evolutionary turnover in the influenza virus, a number of studies have successfully forecasted the dominant circulating strains for a one year period [3]}, [4]}. Similarly, phenotypic convergence across parallel evolving populations has been reported as an evidence for phenotypic predictability, despite a wide genotypic divergence [5]}, [6]}, [7]}, [8]}. Therefore, to exploit the evolutionary predictability for the purpose of control, it is essential to identify the relevant degrees of freedom (e.g., phenotypes vs. genotypes) that confer predictive information and to characterize the evolutionary time scales over which our observations from a population can inform our interventions to drive the future evolution.
